Summary: A Layered Approach to People Detection in 3D Range Data
Luciano Spinelloa,b Kai O. Arrasa Rudolph Triebelb Roland Siegwartb
aSocial Robotics Lab, University of Freiburg, Germany
bAutonomous Systems Lab, ETH Zurich, Switzerland
Abstract
People tracking is a key technology for autonomous systems,
intelligent cars and social robots operating in populated envi-
ronments. What makes the task difficult is that the appearance
of humans in range data can change drastically as a function
of body pose, distance to the sensor, self-occlusion and oc-
clusion by other objects. In this paper we propose a novel ap-
proach to pedestrian detection in 3D range data based on su-
pervised learning techniques to create a bank of classifiers for
different height levels of the human body. In particular, our
approach applies AdaBoost to train a strong classifier from
geometrical and statistical features of groups of neighboring
points at the same height. In a second step, the AdaBoost
classifiers mutually enforce their evidence across different
heights by voting into a continuous space. Pedestrians are fi-
nally found efficiently by mean-shift search for local maxima